AI Research Leader with 16+ years of experience (12+ in industry, 4+ in PhD research) driving enterprise-grade AI/ML and Generative AI from idea to large-scale deployment for global enterprises and Fortune-500 customers.
Known for bridging deep research with business strategy—translating cutting-edge work in LLMs, Agentic AI, time-series forecasting, and causal/explainable AI into production systems that improve competitiveness, resilience, and revenue impact.
Track record includes 14+ patents (granted/filed), 20+ peer-reviewed publications, and multi-million-dollar AI portfolios spanning Conversational AI, Telecom (5G/IoT), Healthcare, Smart Infrastructure, Security, and HR Tech.
Experienced in building, scaling, and retaining high-performing research teams, partnering with executives and product leaders, and owning multi-year roadmaps that balance innovation with deliverables.
Built and led global AI research teams from 5 to ~20 researchers and engineers while maintaining ~90% retention, mentoring senior scientists, and establishing a culture of innovation and delivery.
Owned and executed multi-year AI/ML roadmaps in Generative AI, Agentic LLMs, and time-series forecasting, aligning with corporate strategy, product lines, and IP portfolios.
Secured and managed $2M+ in R&D funding, collaborating with leadership on portfolio prioritization, budget planning, and investment trade-offs across multiple AI initiatives.
Consistently converted research into production, improving research-to-production conversion rate by 25% YoY, with multiple systems deployed in 5G networks, RCA, forecasting, and Conversational AI.
Recognized thought leader: reviewer / PC member for AAAI, IEEE, ACM, ECML-PKDD, CODS-COMAD, with a strong record of shaping research in Generative AI, Explainable AI, and security.
R&D & Product Leadership: AI/ML portfolio ownership · multi-year roadmap design · Stakeholder alignment (CTO, Product, Sales) · Scaled Agile / iterative delivery · KPI/OKR design · Risk and dependency management
People & Organization: Building & scaling research teams · Coaching senior scientists · Performance management · Cross-functional collaboration with engineering & product · Hiring & retention strategy
Technical & Innovation: Generative AI & LLMs · Agentic AI · Multi-modal LLMs · Reinforcement Learning · Time-series forecasting (multivariate, spatiotemporal, multi-step, cold-start) · RCA & Causal Learning · Explainable / Trustworthy AI
Business Domains: Telecom (5G/IoT) · Smart Infrastructure & Disaster Response · Conversational AI & Commercial NLP · Security & Cyber Hate Detection · Healthcare (Adverse Events, Bioinformatics) · HR Tech & Workforce Intelligence
Ph.D., Computer Science – IIIT Hyderabad, India | Thesis focus: intelligent text mining under limited linguistic resources (summarization, keyphrase extraction, question answering).
Post-Doctoral Research, Computer Science – University of California, Davis, USA | Focus: AI-driven knowledge taxonomy and ontology learning for software engineering documentation.
MCA and BCA – Indira Gandhi National Open University (IGNOU), India.
Apr 2022 – Present: R&D Team Manager | Fujitsu Research India Pvt. Ltd.
Spearheading strategy, talent development, and delivery of cutting-edge solutions in Generative AI, Agentic LLMs, Multi-Modal LLMs, Reinforcement Learning, and AI/ML.
Driving cross-functional collaboration with global R&D and product teams to ensure research breakthroughs are seamlessly transitioned into scalable business applications.
Mentoring senior researchers and technical leads, fostering innovation culture while aligning projects with organizational goals and client expectations.
Orchestrating multi-year research roadmaps that balance cutting-edge exploration with near-term enterprise adoption and commercialization.
Leading the scaling of high-performance research teams, translating innovations into enterprise-ready solutions, patents, publications, and new product lines driving measurable business impact.
Key Achievements (9 Patents Filed | 3 Publications | Multiple Enterprise Deployments):
Developed a novel RCA pipeline for log/text data; research accepted at ACL 2025 Findings (HG-InsightLog: Context Prioritization and Reduction for Question Answering with Non-Natural Language Construct Log Data).
Agentic AI and Neuro Symbolic AI supported Advanced root cause analysis for log/text data predictive fault resolution.
Designed explainable time-series forecasting and anomaly detection models using LLMs and deep learning.
Designed Agentic LLM and Self Supervised RL base Natural Language to SQL generation system.
Worked on Programmatic Generative Temporal Intelligence (PGTI) - where Generative AI systems engage with temporal data.
Optimized training strategies (Curriculum Learning) to significantly improve multivariate forecasting accuracy.
Built multi-step, multi-variate forecasting models for 5G network usage; results published at IEEE Globecom 2023 (IEEE Flagship Conference).
Pioneered methods for multivariate time series forecasting in cold-start scenarios.
Engineered anomaly detection frameworks for time-series data; published work at ACM WiSec 2024.
Designed and deployed an AI-powered real-time storm tracking and chaser system to support disaster response.
Created a causal graph-based prediction framework for large-scale gene expression systems, driving insights in bioinformatics.
Increased research-to-production conversion rate by 25% year-over-year, accelerating the path from ideation to enterprise deployment.
Grew the research team from 5 to 20 members while maintaining an average team retention rate of 90%, well above industry benchmarks.
May 2020 – Apr 2022: Senior Chief Engineer II & Manager, Samsung R&D, Bangalore
Directed, coached, and advised cross-functional teams in Conversational AI, NLP, Deep Learning, and Machine Learning, driving high-impact research and enterprise-focused innovations.
Key Achievements:
Conversational AI Innovations:
Architected and launched a Scalable Multi-Intent Recognition System for Samsung’s Conversational AI platform, published in IEEE Access.
Delivered advanced Out-of-Domain Intent Detection and Missing Slot Detection frameworks, securing a granted US Patent (US20230077874 A1) and multiple peer-reviewed publications.
Pioneered next-generation Conversational AI capabilities through an Emotionally Intelligent Dialogue System and Speech-based Age & Gender Prediction models.
IoT Risk Mitigation:
Conceived and patented an AI-powered Accident Prediction & Smart-Home Support System (US20230402187A1), enhancing safety, reliability, and trust in connected IoT ecosystems.
Apr 2018 – May 2020: IT Research Scientist III (Senior Scientist), Conduent Labs, Bangalore, India
Led high-impact research initiatives with strong business alignment, mentored junior researchers, and drove innovation through cutting-edge AI/ML projects.
Key Achievements:
Predictive Analytics & Public Safety: Invented an Automatic Crime Volume Prediction System (US Patent: US20200410321A1).
AI for Social Good: Developed an Automatic Hate Target Identification System (US Patent: US20210240938A1) and an Automated Cyber Hate Profiling System, the latter earning company-wide recognition and awards.
Responsible AI Research: Built a Code-Mixed Language Model for Aggression Detection, presented at COMAD 2020.
Healthcare AI Innovation: Designed an Adverse Drug Event Detection System, securing 1st Prize in Conduent’s 3i HUB Innovation Initiative (Innovate, Implement, Impact).
Affective Computing: Patented methods for Quantifying Emotion and Sentiment (US Patent: US20230325604A1).
Conversational AI Advancement: Engineered an Automated Bot-Humanization System for Conduent BOT-DARA, enhancing dialogue quality and user trust.
Dec 2016 – Feb 2018: Senior Machine Learning Scientist, Phenom People
Spearheaded social graph analytics initiatives to optimize workforce planning, enhance process efficiency, and drive product adoption across enterprise solutions.
Key Achievements:
Engineered a weighted knowledge graph powering Smart B2B2C hiring, context-aware search, and personalized search experiences, improving talent acquisition outcomes.
Designed and implemented a Candidate Social Graph enabling automatic resource allocation and strategic workforce planning, complemented by an Automatic Job-Highlight System that streamlined recruiter decision-making. Ensured materials were handled and dispatched following health and safety protocols.
Sep 2015 – Sep 2016: Post-Doctoral Researcher, University of California, Davis
Worked on research project on automated AI-driven taxonomy extraction from software engineering documentation. Produced actionable frameworks for knowledge mining in engineering repositories.
May 2013 – Aug 2015: Research Professional, TCS Innovation Lab, New Delhi
Developed and published award-winning systems for automatic plagiarism detection. (winner of the best paper award 1st place @ CICLing-2014).
Automatic text-quality grading system (Effectively grades the quality of E-mails, without relevant background model). (Paper published).
Automatic event detection, alignment and prediction system (related to economic events).
May 2012 – Jul 2012: Research Intern, IBM India Research Lab (IRL), Bangalore
Developed advanced “Why-based” question answering using Wikipedia, improving natural language Q&A capabilities for enterprise search.
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AI / ML Techniques & Domains: Generative AI, Large Language Models (LLMs), Agentic AI, Multi-Modal LLMs, Reinforcement Learning, Traditional AI-ML, Conversational AI, Natural Language Processing (NLP), Deep Learning (DL), Time-Series Forecasting (Multivariate, Spatiotemporal, Multi-step, Cold-start), Explainable AI (XAI), Trustworthy AI, Root Cause Analysis (RCA), Causal Learning / Causal Graph Prediction, Anomaly Detection, Cyber Hate Profiling, Emotion & Sentiment Analysis, Emotionally Intelligent Dialogue Systems, Age & Gender Prediction (Speech), Social Graph Analytics, Knowledge Graphs (Weighted, Contextual, Personalized), Plagiarism Detection, Text Quality Grading, Automatic Event Detection, Alignment & Prediction, Taxonomy Extraction (AI-driven), Crime Volume Prediction, Hate Target Identification, Candidate Social Graph for Workforce Planning
Application Areas / Industry Use-Cases: 5G Network Usage Forecasting, IoT Risk Mitigation, Smart Infrastructure (Automated Real-Time Storm Chaser), Gene Expression Prediction, Adverse Drug Event Detection, Hiring / HR Tech (Smart B2B2C Hiring, Job Highlight System), Cybersecurity (Network Intrusion Detection, IoT Safety, Log/Text RCA, Cyber Hate)
Frameworks / Models / Methods: Mamba/Jamba (for Log/Text RCA with LLMs), Graph-based Models (Summarization, Keyphrase Extraction, Clustering, Plagiarism Detection), Unsupervised Deep Semantic & Logical Analysis, Code-Mixed Language Models, Adversarial Training (Anomaly/Intrusion Detection), Smart Stacking of Deep Learning Models (Intent-Slot Extraction)
BEST PAPER AWARD (1st place) @ CICLING-2014 – Details: “Niraj Kumar; “A Graph-Based Automatic Plagiarism Detection Technique to Handle The Artificial Word Reordering and Paraphrasing”, A. Gelbukh (Ed.): CICLing 2014, LNCS 8404, pp. 481–494, 2014. (LINK)
IN TOP SYSTEM @ TAC-2011: My system was in the top system for “Automatic Summarization Evaluation Task” at Text Analysis Conference (TAC 2011), organized by National Institute of Standards and Technology (NIST), Gaithersburg, Maryland, USA TAC-2011. (For details, see My Publications)
IN TOP SYSTEM @ TAC-2010: My system was in the top system for “Automatic Summarization Evaluation Task” at Text Analysis Conference (TAC 2010), organized by National Institute of Standards and Technology (NIST), Gaithersburg, Maryland, USA TAC-2010. (For details, see My Publications).
Other Recognition: Our Unsupervised Phrase Identification technique for Keyphrase Extraction, has been appreciated by the survey paper published in COLING-2010, Titled: “Conundrums in Unsupervised Keyphrase Extraction: Making Sense of the State-of-the-Art”
Program Committee Member: (1) CODS-COMAD – 2018, 2019, 2020, 2021, 2022 (2) ICON 2013, 2016, 2020, 2021, (3) AI-ML Systems - 2022
Reviewed Journal paper: Oxford Journals -> Science & Mathematics -> “Computer Journal”, IEEE ACCESS.
US20230402187A1: Method and system for mitigating physical risks in an IoT environment (Samsung, 2023)
US20230077874A1: Methods and systems for determining missing slots in voice interaction (Samsung, 2023)
US20200410321A1: Neural network systems and methods for event parameter determination (Conduent, 2020)
US20230325604A1: Method and system for automated sentiment classification (Conduent, 2023)
US20210240938A1: Neural network systems and methods for target identification from text (Conduent, 2021)
US20250173564A1: Spatiotemporal Multi-Step and Multi-Variate time series forecasting for 5G Network usage (Fujitsu, 2025)
US20250048144A1: Cold-Start Multivariate Time Series Forecasting (Fujitsu, 2025)
(Filed @ Fujitsu Research India, 2022–2025):
Proactive RCA from Log/Text Data with Mamba and LLM
Explainable Timeseries forecasting & Anomaly Detection
Automated Real-Time Storm Chaser System
Causal Graph Linked Prediction for Gene Expression
Enhancing Multivariate Time Series Training Strategies
Anomaly Detection in Timeseries Data
and more (Full list available upon request).
Niraj Kumar, Bhiman Kumar Baghel, “Intent Focused Semantic Parsing and zero-shot Learning for Out-of-Domain detection in Spoken Language Understanding” has been accepted for publication in IEEE Access. [Dec-2021]
Niraj Kumar, Bhiman Kumar Baghel, “Smart Stacking of Deep Learning Models for Granular Joint Intent-Slot Extraction for Multi-Intent SLU” has been accepted for publication in IEEE Access. [June-2021]
Niraj Kumar, Kannan Srinathan and Vasudeva Varma; “Unsupervised Deep Semantic and Logical Analysis for Identification of Solution Posts from Community Answers”; “Int. J. of Information and Decision Sciences”, IJIDS 8(2): 153-178 (2016).
Niraj Kumar, Kannan Srinathan and Vasudeva Varma; “A Graph based Unsupervised N-gram Filtration Technique for Automatic Keyphrase Extraction”; “Int. J. of Data Mining, Modelling and Management”, Vol. 8, No. 2: 124-143, (2016)
Supriya Bajpai, Athira Gopal, Chandrakant, Niraj Kumar; HG-InsightLog: Context Prioritization and Reduction for Question Answering with Non-Natural Language Construct Log Data. Accepted at ACL-2025 Findings.
Supriya Bajpai, Krishna Murthi, Niraj Kumar, AnomGraphAdv: Enhancing Anomaly and Network Intrusion Detection in Wireless Networks Using Adversarial Training and Temporal Graph Networks. Accepted for publication in ACM WiSec 2024.
Nikhil Cherian Kurian, Niraj Kumar; Improved Multi-Step, Multi-Variate, and Spatiotemporal 5G Data Usage Forecasting Without Deploying Data Imputation Techniques; 2023 IEEE Global Communications Conference.
Anant Khandelwal, Niraj Kumar; A Unified System for Aggression Identification in English Code-Mixed and Uni-Lingual Texts. COMAD/CODS 2020: 55-64.
Niraj Kumar; “A Graph Based Automatic Plagiarism Detection Technique to Handle the Artificial Word Reordering and Paraphrasing” CICLing 2014, LNCS 8404, pp. 481–494, 2014. (My work @ TCS Innovation Lab; best paper award, 1st place @ CICLing 2014).
Niraj Kumar and Lipika Dey; “Automatic Quality Assessment of documents with Application to Essay grading”; accepted for publication in MICAI-2013. (My work @ TCS Innovation Lab).
Niraj Kumar, Kannan Srinathan, Vasudeva Varma: A Knowledge Induced Graph-Theoretical Model for Extract and Abstract Single Document Summarization. CICLing (2) 2013: LNCS 7817, pp. 408-423.
Niraj Kumar, Rashmi Gangadharaiah., Kannan Srinathan and Vasudeva Varma; “Exploring the Role of Logically Related Non-Question Phrases for Answering Why-Questions”; Accepted for publication in NLDB-2013.
Niraj Kumar, Kannan Srinathan, and Vasudeva Varma; Using Graph Based Mapping of Co-Occurring Words and Closeness Centrality Score for Summarization Evaluation; A. Gelbukh (Ed.): CICLing 2012, LNCS 7182, pp. 353–365, 2012.
Niraj Kumar, Kannan Srinathan, and Vasudeva Varma; Using Wikipedia Anchor Text and Weighted Clustering Coefficient to Enhance the Traditional Multi-Document Summarization; A. Gelbukh (Ed.): CICLing 2012, LNCS 7182, pp. 390–401, 2012.
Niraj Kumar, Kannan Srinathan, and Vasudeva Varma; Using Unsupervised System with least linguistic features for TAC-AESOP Task; In: Proceedings of Text Analysis Conference (TAC 2011), National Institute of Standards and Technology (NIST), Gaithersburg, Maryland, USA TAC-2011.
Niraj Kumar, Kannan Srinathan, and Vasudeva Varma; An Effective Approach for AESOP and Guided Summarization Task; In: Proceedings of Text Analysis Conference (TAC 2010), National Institute of Standards and Technology (NIST), Gaithersburg, Maryland, USA TAC 2010.
Niraj Kumar, Kannan Srinathan and Vasudeva Varma; Evaluating Information Coverage in Machine Generated Summary and Variable Length Documents; COMAD 2010.
Niraj Kumar, Venkata Vinay Babu Vemula, Kannan Srinathan, Vasudeva Varma: Exploiting N-gram Importance and Wikipedia based Additional Knowledge for Improvements in GAAC based Document Clustering. KDIR 2010: 182-187.
Niraj Kumar, Kannan Srinathan and Vasudeva Varma; Key Fact Extraction from Newswire Articles by Exploiting Local features based weighting and Interaction of sentences,(Published in ICON-2010, length 6-pages)
Niraj Kumar and Kannan Srinathan; A New Approach for Clustering Variable Length Documents,(Published in IEEE IACC-09).
Niraj Kumar, Kannan Srinathan: Automatic keyphrase extraction from scientific documents using N-gram filtration technique. ACM Symposium on Document Engineering 2008: 199-208.